Evaluating the Contribution of Sporosarcina to Carbonate Precipitation in Anaerobic Soils: A Microbial Community and Quantitative Analysis

Abstract

Microbially induced calcite precipitation (MICP) has attracted attention as an environmentally friendly soil stabilization method, with Sporosarcina pasteurii being a key ureolytic bacterium in this process. However, its behavior in oxygen-limited environments remains poorly understood, limiting the predictability of MICP outcomes in natural soils. This study investigated the population dynamics of Sporosarcina in compacted soil reactors operated under aerobic and anaerobic conditions, including saturated environments. Quantitative PCR and 16S rRNA gene sequencing revealed that Sporosarcina thrived and became dominant under aerobic, unsaturated conditions, but failed to maintain a high abundance under anaerobic or saturated conditions. These findings indicate that gas-phase oxygen—not merely its presence in the overlying atmosphere—is essential for effective Sporosarcina-driven MICP. The results highlight a critical environmental constraint that limits the application of biostimulation strategies relying on indigenous Sporosarcina in oxygen-poor soils. This study provides the first in situ evidence linking oxygen availability and microbial dominance in MICP systems, with implications for optimizing microbial soil stabilization in real-world conditions.

1. Introduction

The prevention of natural disasters, including landslides, is a critical issue in civil engineering in Japan. Notable examples of landslides include one caused by the 2024 Noto earthquake [1], which resulted in numerous fatalities, and another triggered by torrential rainfall in western Japan in July 2018 [2]. Japan’s terrain is predominantly mountainous, with mountains comprising ~75% of the country’s land area. These mountainous regions often have soft ground, making the soil prone to liquefaction during earthquakes and heavy rains, which can lead to landslides and related disasters [3]. Current soil stabilization methods include various engineering techniques based on different principles, such as sand compaction, deep mixing, deep wells, gravel drains, and chemical injection. The chemical injection methods are primarily used as auxiliary, temporary measures due to challenges like the uneven strength and poor durability of the solidified mass resulting from crack propagation during injection [4,5]. However, recent advancements have transformed the chemical injection from a temporary stopgap into a more permanent soil stabilization solution. Innovations such as simplified equipment and operation, combined with new low-viscosity, high-durability silica-based chemical grouts, have paved the way for using chemical injection to permanently stabilize soft ground beneath existing structures [6].

Nonetheless, the use of chemical grouts (e.g., cement milk) poses environmental and technical challenges. They generally become strongly alkaline after solidification, leading to restrictions on their use in environmentally sensitive areas like farmland and residential zones [7]. In addition, injecting chemical grouts into the ground can cause clogging within the soil matrix, which limits the grout’s effectiveness by preventing a uniform permeation and resulting in an incomplete stabilization of the target area [8]. Biogrout, which leverages the microbially induced calcite precipitation (MICP) process, has emerged as a promising solution for soil stabilization because it minimizes the risk of the soil matrix clogging [9]. At the heart of this process is Sporosarcina pasteurii, an organism known for its ability to catalyze the hydrolysis of urea into carbon dioxide and ammonia—a fundamental step in calcite formation when calcium ions are present [10]. This urease-driven activity occurs under both aerobic and anaerobic conditions, demonstrating the enzyme’s adaptability [11]. However, fully harnessing the metabolic potential of Sporosarcina for effective MICP relies heavily on the presence of oxygen [12].

Oxygen, as the prime electron acceptor in aerobic respiration, enables efficient ATP production in Sporosarcina, thereby supporting its growth and robust urease synthesis. In anaerobic environments, Sporosarcina faces metabolic challenges and pivots to less-efficient pathways for energy conservation. Nitrate can serve as an alternative electron acceptor to support anaerobic respiration [13]; however, nitrate is intrinsically scarce in such oxygen-depleted systems, and without oxygen the conversion of the ammonia to nitrate is severely bottlenecked [14]. Even anaerobic ammonium oxidation (ANAMMOX), which directly oxidizes ammonia to nitrogen gas, requires nitrite as an electron acceptor—underscoring the essential role of oxygen in supplying these intermediates [15]. This fundamental limitation, including the unresolved challenge of the urea metabolism under strict anaerobic conditions [16], greatly constrains the metabolic activity of Sporosarcina in oxygen-deprived environments.

To date, in the context of soil stabilization by biostimulation (using in situ Sporosarcina), no studies have tracked the population dynamics of Sporosarcina within natural soil microbial communities under oxygen-limited conditions. This research is the first to empirically monitor Sporosarcina’s in situ population dynamics under such conditions. By comparing its dominance in saturated versus unsaturated soils across both aerobic and anaerobic regimes, we provide novel ecological insights into the role of Sporosarcina in biostimulation-based MICP—an aspect previously unexplored in the literature.

2. Materials and Methods

2.1. Reactor Operational Conditions

2.1.1. Continuous Reactors

The enrichment of ureolytic bacteria under anaerobic conditions was examined by using a culture medium from which dissolved oxygen had been removed. Dissolved oxygen was eliminated with an anaerobic gas-replacement apparatus that flushed the reactor with inert gas at a low flow rate. Glass column reactors (30 mL volume, KF-30, AS-ONE, Osaka, Japan) were used; each was packed with 50 g of soil (collected from the National Institute of Technology, Kure College campus, coordinates E132°36′21″, N34°13′44″), compacted to serve as the inoculum source for the urea-hydrolysis system. To examine the effect of oxygen, two glass column reactors were operated in parallel: one under aerobic conditions and one under anaerobic conditions. Anaerobic conditions were maintained using an AnaeroPack Kenki (Mitsubishi Gas Chemical, Tokyo, Japan), placed in a modified sealed chamber (A-41, Sugiyama-Gen, Tokyo, Japan). The culture medium was supplied to each reactor at a low rate (30 mL/day) over a 12-day period. The effluent was drained through PharMed tubing into a sealed container with a gas-collection bag, effectively preventing oxygen ingress from the environment. The composition of the culture medium was as follows (per liter): urea 21.022 g, sodium acetate 5.784 g, ammonium chloride 0.67 g, yeast extract 0.1 g, and calcium chloride 36.754 g. (The medium was filter-sterilized to avoid urea decomposition by autoclaving.) For the anaerobic reactor, the medium was further subjected to vacuum degassing and nitrogen gas exchange using a gas-exchange apparatus (Sanshin Kogyo, Tokyo, Japan) (Figure 1). In contrast, the aerobic reactor received the same medium without gas exchange, and its effluent was collected under open-air conditions. Although the oxygen concentration was not directly measured, anaerobic conditions were assumed based on the use of these widely accepted methodological standards. This assumption is further supported by the results described later in this study.

2.1.2. Batch Reactors

Using the same soil as described above, the batch reactors were constructed in 50-mL serum bottles. Each bottle received 10 g of soil and 30 mL of the culture medium. This setup was used to replicate saturated soil conditions—where oxygen supply is extremely limited due to water-filled pore spaces—by completely submerging the soil in the medium. Three types of systems were established: an open-air bottle exposed to atmospheric conditions (aerobic), a fully anaerobic bottle achieved by nitrogen gas exchange, and an anaerobic bottle incubated in a sealed box with an AnaeroPack. After 12 days of incubation, the soil samples were retrieved from each system for analysis.

2.2. DNA Extraction and Community Structure Analysis

Genomic DNA was extracted from both continuous reactors (aerobic and anaerobic) and from the day-12 batch reactor samples using a MonoFas Bacterial Genomic DNA Extraction Kit VII (GLC Science, Tokyo, Japan), according to the manufacturer’s instructions. The concentration and purity of the DNA were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Using these DNA samples as templates, the V3–V4 variable regions of the 16S rRNA gene were amplified with a KAPA HiFi HotStart ReadyMix kit (Kapa Biosystems, Wilmington, MA, USA) using bacterial domain-specific primers 341F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′) and 805R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′) [17]. The PCR program consisted of initial denaturation at 94 °C for 2 min; 25 cycles at 95 °C for 10 s, 55 °C for 30 s, and 72 °C for 30 s; and a final extension at 72 °C for 5 min. The resulting ~450 bp amplicons were purified using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA). The purified amplicons were then indexed by a second PCR using a Nextera XT Index Kit v2 Set A (Illumina, San Diego, CA, USA), followed by another round of purification as described above. The indexed libraries were quantified with a Promega Quantus fluorometer (Promega, Madison, WI, USA), normalized, and pooled. The mixed library was sequenced (2 × 300 bp paired-end) on an Illumina MiSeq platform using a MiSeq Reagent Kit v3. Raw sequencing data were processed using the 16S Metagenomics App on the Illumina BaseSpace Sequence Hub (Illumina, San Diego, CA, USA), which applies the RDP Classifier algorithm to assign taxonomy against the Illumina-curated GreenGenes database (v13.5) [18]. The pipeline performs internal quality filtering and chimera removal. Output data were automatically normalized to relative abundances summing to 100% per sample. Genera not detected in a sample were assigned a value of zero for downstream analyses.

The genus-level abundance data derived from high-throughput 16S rRNA sequencing were subjected to a non-metric multidimensional scaling (NMDS) analysis based on Bray–Curtis dissimilarity to explore the overall structure and temporal dynamics of the bacterial communities across different oxygen and moisture conditions. To ensure comparability among samples, the dataset was normalized such that the total relative abundance for each sample summed to 100%, and genera not detected in a given sample were assigned a value of zero. Bray–Curtis dissimilarity matrices were calculated, and an NMDS was performed using the sklearn.manifold.MDS function in Python (scikit-learn version 1.4.1) with metric = False to obtain both two-dimensional solutions.

2.3. qPCR Conditions

qPCR was used to determine the total bacterial abundance. Amplifications were performed with universal 16S rRNA gene primers UNI331F and UNI797R, which effectively amplify a conserved region of the 16S rRNA gene present in most bacteria. The reactions were run on a StepOne Real-Time PCR System (Life Technologies, Carlsbad, CA, USA) using the SYBR Premix Ex Taq II kit (TaKaRa Bio, Shiga, Japan). Each 20 μL reaction (run in triplicate for precision) contained 10 μL of 2× SYBR Premix, 0.2 μM of each primer, and 1 μL of template DNA. The qPCR thermal program consisted of an initial 95 °C denaturation for 20 s; 40 cycles of 95 °C for 5 s, annealing at an appropriate temperature for 30 s, and 72 °C for 35 s; followed by a final 94 °C for 15 s. The fluorescence was measured at the end of each cycle. For quantification, a standard curve was generated using a plasmid containing the target amplicon at the known copy number. The cycle threshold (Ct) values were converted to gene copy numbers as described previously [19]. The error bars shown in the corresponding Figure 2 represent the standard deviation of triplicate qPCR measurements.

To estimate the Sporosarcina-specific 16S rRNA gene copy numbers, we multiplied Sporosarcina’s relative abundance (from MiSeq sequencing) by the total bacterial 16S rRNA gene copy number obtained via qPCR. This approach assumes that the proportion of sequencing reads is proportional to the proportion of cells in the community, allowing a semi-quantitative comparison of Sporosarcina population sizes across the conditions. Note that this estimate was not subjected to statistical analysis due to the compositional nature of sequencing-based relative abundance data.

To evaluate the effects of the oxygen condition (aerobic vs. anaerobic), incubation time (0–12 days), and their interaction on bacterial abundance, a two-way analysis of variance (ANOVA) was performed using Python (version 3.10) with the statsmodels package. The response variable was the Ct value obtained from the qPCR, which is a direct experimental measurement prior to the transformation into the gene copy number and allows for a valid statistical comparison. The qPCR reactions were conducted in technical triplicate, and the mean Ct values were used in the statistical analysis. All statistical procedures were conducted using an ordinary least squares (OLS) factorial design (Ct ~ C(Oxygen) * C(Day)), and ANOVA tables were generated using the anova_lm() function in statsmodels. The significance was determined at p < 0.05. The raw Ct values used in this analysis, along with triplicate replicates, have been deposited in Figshare (DOI: https://doi.org/10.6084/m9.figshare.29123963.v1).

2.4. Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this article, the corresponding author, Kimura, used SciSpaceGPT for English proofreading. After using this tool, the corresponding author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

3. Results and Discussions

3.1. Comparative Dynamics of Aerobic and Anaerobic Bacterial Populations

The results shown in Figure 2 suggest that little to no net increase in the anaerobic bacterial population occurred over the 12-day experimental period. Under aerobic conditions, the total bacterial 16S rRNA gene copy number increased by nearly two orders of magnitude—representing an approximate 100-fold increase from day 0 to day 12. In contrast, the anaerobic condition showed only a modest increase (less than 5-fold), plateauing after day 9. This dramatic aerobic growth could be attributed to several factors. In the presence of oxygen, the microbes can utilize more efficient metabolic pathways (such as aerobic respiration), enabling faster growth and higher cell densities. Additionally, the medium contained readily metabolizable substrates like acetate, which served as the primary organic carbon and energy source for the bacteria once the urea was hydrolyzed. Thus, the energy demands of the bacterial community were met predominantly by acetate under aerobic conditions.

Despite the limited growth observed under the anaerobic conditions, the bacteria did not significantly proliferate. This outcome is likely due to the absence of oxygen, which forced cells to rely on less-efficient anaerobic respiration pathways or other electron acceptors. Because the medium initially contained no carbonate ions and the headspace gas was replaced with nitrogen, the only electron acceptors available were those produced via urea hydrolysis. The soil itself, however, could provide alternative electron acceptors such as humic substances [20] or metal oxides [21], even if these are less accessible than oxygen. These factors (limited electron acceptors and their availability) help explain the stark contrast in growth between aerobic and anaerobic conditions.

The differences in the bacterial population trends between the aerobic and anaerobic conditions were further evaluated using a two-way ANOVA. This analysis assessed the effects of the oxygen condition (aerobic vs. anaerobic), time (days), and their interaction on the 16S rRNA gene Ct values obtained by the qPCR (

Table 1. Two-way ANOVA results for qPCR-derived Ct values. Summary of statistical effects of oxygen condition, incubation time, and their interaction on total bacterial abundance.

Factor	F-Value	p-Value	Interpretation
Condition (Aerobic vs. Anaerobic)	78.18	1.33 × 10⁻9	Significant: Ct values (i.e., bacterial counts) differ by oxygen conditions
Time (days)	45.88	1.27 × 10⁻9	Significant: Ct values change over time
Interaction (Condition × Time)	5.81	6.40 × 10⁻4	Significant: Temporal patterns of bacterial growth differ by condition

). The ANOVA results showed statistically significant effects for all three factors. Specifically, the oxygen availability had a highly significant impact (F = 78.18, p = 1.33 × 10⁻⁹), indicating that the bacterial abundance (as reflected by the Ct values) differed consistently between the aerobic and anaerobic environments. Time also had a significant effect (F = 45.88, p = 1.27 × 10⁻⁹), confirming that the bacterial populations changed appreciably over the 12 days of the incubation. Importantly, the interaction between oxygen and time was significant (F = 5.81, p = 6.40 × 10⁻⁴), suggesting that the temporal pattern of bacterial growth depended on whether oxygen was present.

In summary, these results demonstrate that oxygen availability drives a nearly 100-fold difference in the total bacterial abundance over time, confirming that microbial proliferation in MICP systems is fundamentally oxygen-dependent.

3.2. Microbial Community Shifts and Sporosarcina Dominance Under Aerobic and Anaerobic Reactor Conditions

The results of the MiSeq 16S rRNA gene sequencing, summarized in

Table 2. Results of MiSeq microbial community analysis. Relative abundances (%) of dominant genera detected by 16S rRNA sequencing across different days and oxygen conditions. Values are expressed as percentage of total sequencing reads per sample.

Day 0	% total reads
Unclassified at genus level	56.08%
Niabella	20.20%
Sphingomonas	12.57%
Sediminibacterium	3.71%
Staphylococcus	3.64%
Bartonella	1.46%
Propionibacterium	1.33%
Candidatus Rhabdochlamydia	0.11%
Other genera	0.90%
Day 1 aerobic	% total reads	Day 1 anaerobic	% total reads
Sporosarcina	77.95%	Sporosarcina	88.68%
Unclassified at genus level	6.66%	Unclassified at genus level	4.57%
Bacillus	3.24%	Bacillus	1.32%
Escherichia	2.44%	Planococcus	1.08%
Enterobacter	2.20%	Paenibacillus	0.59%
Brevibacillus	1.96%	PaeniSporosarcina	0.54%
Serratia	0.77%	Achromobacter	0.39%
Planococcus	0.67%	Lysobacter	0.37%
Other genera	4.11%	Other genera	2.46%
Day 3 aerobic	% total reads	Day 3 anaerobic	% total reads
Escherichia	21.20%	Unclassified at genus level	34.58%
Stenotrophomonas	14.40%	Sporosarcina	20.55%
Unclassified genus level	14.30%	Bacillus	6.36%
Xanthomonas	13.70%	Peptoniphilus	3.92%
Enterobacter	11.98%	Dolichospermum	3.10%
Achromobacter	8.67%	Serratia	3.10%
Brevibacillus	5.02%	Escherichia	2.61%
Sporosarcina	2.60%	Brevibacillus	2.12%
Other genera	8.13%	Other genera	23.66%
Day 6 aerobic	% total reads	Day 6 anaerobic	% total reads
Sporosarcina	54.56%	Unclassified at genus level	39.35%
Achromobacter	9.18%	Sporosarcina	10.75%
Unclassified at genus level	7.78%	Bacillus	8.11%
Peptoniphilus	5.15%	Peptoniphilus	3.25%
Natronincola	4.74%	Xanthomonas	2.43%
Enterobacter	3.93%	Serratia	2.43%
Xanthomonas	2.81%	Alkaliphilus	2.43%
Serratia	2.35%	Natronincola	1.83%
Other genera	9.50%	Other genera	29.42%
Day 9 aerobic	% total reads	Day 9 anaerobic	% total reads
Sporosarcina	45.06%	Sporosarcina	23.29%
Unclassified at genus level	26.01%	Unclassified at genus level	18.35%
Achromobacter	6.89%	Bacillus	18.20%
Pullulanibacillus	5.80%	Peptoniphilus	15.38%
Peptoniphilus	3.91%	Virgibacillus	6.44%
Bacillus	2.12%	Natronincola	4.49%
Natronincola	1.92%	Achromobacter	2.77%
Enterobacter	1.46%	Desulfurispora	1.50%
Other genera	6.83%	Other genera	9.58%
Day 12 aerobic	% total reads	Day 12 anaerobic	% total reads
Sporosarcina	75.33%	Unclassified at genus level	25.33%
Unclassified at genus level	6.30%	Virgibacillus	16.90%
Peptoniphilus	5.10%	Peptoniphilus	15.87%
Achromobacter	3.48%	Bacillus	15.55%
Natronincola	1.83%	Sporosarcina	5.74%
Pullulanibacillus	1.31%	Natronincola	5.18%
Planococcus	1.06%	Lentibacillus	1.63%
Enterobacter	0.72%	Lactobacillus	1.49%
Other genera	4.87%	Other genera	12.31%

, provide a detailed view of the microbial community dynamics over the 12-day period in the continuous reactors. On day 0, a large fraction of the community could not be classified at the genus level, suggesting a diverse initial microbiota. As the experiment progressed, Sporosarcina became increasingly dominant under aerobic conditions. Notably, a very high relative abundance of Sporosarcina (~88–89%) was also observed under anaerobic conditions by day 1, slightly exceeding its level in the aerobic reactor (~78%). This finding, however, coincided with a temporary reduction in the total bacterial abundance (Figure 2), potentially caused by a downward flow washout effect in the reactor [22]. Given the initial soil-based inoculum and the possible presence of residual oxygen, this early peak likely reflects a brief proliferation event or selective persistence rather than sustained metabolic activity.

This ecological persistence highlights the potential for Sporosarcina to endure environmental stress. In the aerobic reactor, genera such as Escherichia and Stenotrophomonas became more prominent by day 3, likely due to the plentiful supply of both nutrients and oxygen [23,24]. These genera are known for their rapid metabolism and ability to quickly exploit the available resources such as acetate (the primary organic carbon source in the medium aside from urea). Urea, although abundant, is immediately hydrolyzed in this system and therefore cannot serve as an energy source. Thus, under aerobic conditions the community’s energy needs were met mainly by acetate.

In the anaerobic reactor, an increase in “unclassified” sequence reads was observed, complicating the interpretation of the community due to taxonomic ambiguities. However, a key observation was the increased abundance of Peptoniphilus (phylum Bacillota [25]), which is known to ferment yeast extract and acetate anaerobically, potentially producing hydrogen in syntrophy with methanogens [26]. The prominence of Peptoniphilus under anaerobic conditions indicates a metabolic shift toward utilizing the available substrates when oxygen is absent. Furthermore, many of the bacteria detected—such as Bacillus, Virgibacillus, Peptoniphilus, and Sporosarcina—are spore-forming members of Bacillota, suggesting that a substantial portion of the community may enter a dormant spore state under anaerobic conditions [27]. It remains unclear whether any genera aside from Sporosarcina in these communities actively precipitate calcite. Notably, among the dominant genera identified after day 6 under anaerobic conditions, none are well-known for MICP, raising an unresolved question in anaerobic biocementation.

To visualize and summarize these compositional shifts across time and oxygen conditions, we conducted a non-metric multidimensional scaling (NMDS) analysis based on Bray–Curtis dissimilarity [28] (Figure 3). The ordination revealed a clear separation between aerobic and anaerobic trajectories. Aerobic samples formed a relatively compact cluster with a gradual progression, indicating constrained but directional succession. In contrast, anaerobic samples diverged sharply along the second axis, indicating broader community restructuring. These NMDS results support the trends observed in Table 2. Under aerobic conditions, Sporosarcina dominance increased over time, while under anaerobic conditions, its relative abundance declined after day 1 and was replaced by genera better suited to fermentation or anaerobic respiration.

In summary, the MiSeq analysis revealed that Sporosarcina maintained a high relative abundance under aerobic conditions, supporting its oxygen preference. Under anaerobic conditions, Sporosarcina exhibited a sharp but transient dominance on day 1, followed by a rapid decline. Specifically, its relative abundance decreased from 88.7% on day 1 to 20.6% on day 3 and 10.8% on day 6—representing more than an eightfold decrease over five days. This pattern suggests that while Sporosarcina may have briefly proliferated under early anaerobic conditions—possibly due to carryover oxygen or readily available substrates—its metabolic activity was not sustained. This interpretation aligns with the qPCR-based estimates presented in Section 3.3, which show that the absolute number of Sporosarcina cells plateaued after day 1 in the anaerobic reactor. These community-level results underscore the temporal instability of Sporosarcina under anaerobic conditions and set the stage for further quantitative analysis of its absolute abundance across time.

3.3. Impact of Oxygen Availability on Sporosarcina Proliferation and MICP Potential

The detection threshold shown in Figure 4 indicates that Sporosarcina was not among the top eight genera on day 0; the eighth-ranked genus constituted only 0.11% of total reads. This implies that Sporosarcina spp. were initially present at <0.1% of the community, corresponding to approximately 10⁵ cells per gram of soil based on qPCR-derived total bacterial abundance.

Under aerobic conditions, a transient decrease in Sporosarcina abundance was observed by day 3, coinciding with a temporary rise of other genera such as Escherichia (as noted in Section 3.2). However, from day 6 onward, Sporosarcina exhibited a clear upward trend and became consistently dominant. Its estimated population increased by approximately 30-fold between day 1 and day 12, reaching over 10⁷ cells per gram of soil. This steady proliferation suggests that Sporosarcina was actively growing and likely metabolically engaged in MICP processes under aerobic conditions. In contrast, under anaerobic conditions, Sporosarcina showed an initial spike by day 1 but plateaued thereafter. The day 1 increase cannot be explained solely by the washout of other taxa, as it also coincided with a transient rise in the absolute gene copy number. A short-lived proliferation event likely occurred, possibly driven by residual oxygen or readily available carbon sources. However, from day 3 onward, the population remained largely static. By day 12, the Sporosarcina count under anaerobic conditions was approximately two orders of magnitude lower than under aerobic conditions (10⁵ vs. 10⁷ cells/g-soil), highlighting a strong growth limitation.

This distinct growth pattern indicates that while Sporosarcina can persist under anaerobic conditions, its proliferation is severely restricted. The lack of readily available electron acceptors, including oxygen and nitrate, likely limited the energy conservation and thus the cell division. It is important to note that 16S rRNA gene sequencing provides only a taxonomic presence and does not directly confirm metabolic activity. However, in the aerobic reactor, the sustained dominance and substantial increase in cell number—coupled with Sporosarcina’s well-documented role in ureolytic calcite precipitation—strongly support the interpretation that the organism was metabolically active and contributing to the MICP. In contrast, the absence of growth beyond day 1 under anaerobic conditions suggests that Sporosarcina was metabolically suppressed, although still detectable. These results corroborate the hypothesis that oxygen availability is a critical determinant of Sporosarcina’s ecological performance and biotechnological utility in MICP-based soil stabilization.

In summary, this study provides novel insights, demonstrating that the dominance of Sporosarcina—which plays a principal role in biostimulation-based MICP—is unequivocally constrained by the lack of oxygen. Although the urease-driven hydrolysis of urea by Sporosarcina is not directly inhibited by the absence of oxygen [11], the lack of alternative electron acceptors under anaerobic conditions markedly impedes its growth and precludes it from dominating the community [29]. These findings confirm a point previously suggested by pure-culture studies (e.g., with Sporomusa spp.): environmental oxygen availability has a critical influence on the efficacy of the MICP when relying on indigenous Sporosarcina. This study is the first to clearly demonstrate that effect in situ, underscoring the importance of oxygen for successful soil stabilization via Sporosarcina-driven MICP.

3.4. Impact of Oxygen Availability on Sporosarcina Proliferation and MICP Potential in Saturated Soil

In deep subterranean unsaturated soils (which often become effectively anaerobic), simply injecting a urea solution does not always result in Sporosarcina dominance—illustrating a potential limitation of biostimulation in such settings. Saturated soils are even more constrained by the oxygen supply, as their pore spaces are completely filled with water, drastically limiting the oxygen diffusion [30]. In these environments, the oxygen availability is largely confined to what can diffuse from any interface with a gas phase, and this is often insufficient even when the overlying atmosphere is aerobic. In this section, we examine the microbial communities present after 12 days of incubation in the soil samples fully immersed in the medium (simulating saturated soil conditions). By comparing these communities (via MiSeq analysis), we can elucidate the effect of limited oxygen diffusion on Sporosarcina’s behavior and the overall MICP potential.

Figure 5 shows that under all tested saturated-soil conditions—(A) aerobic (open-air), (B) anaerobic with continuous gas exchange (nitrogen-flushed), and (C) anaerobic in a sealed AnaeroPack system—Sporosarcina did not become dominant. This finding contrasts sharply with the overwhelming predominance of Sporosarcina observed under aerobic unsaturated conditions (see Section 3.2). The failure of Sporosarcina to dominate even the “aerobic” saturated condition suggests that the mere presence of the oxygen in the atmosphere is not enough; the presence of a gas phase within the soil (i.e., air-filled pores) is essential for Sporosarcina dominance. The importance of having a gas phase manifests in two ways: (i) it alleviates the oxygen limitation by allowing better O₂ penetration, and (ii) it introduces drying-rewetting cycles. Periodic drying concentrates dissolved ions, potentially enhancing subsequent calcite precipitation upon rewetting [31]. Once the precipitation occurs, the microenvironmental pH rises [32], creating conditions conducive to Sporosarcina growth. As a bacterium capable of thriving at pH > 11, Sporosarcina can then outcompete other genera and achieve dominance [33]. This sequence suggests a positive feedback mechanism in the MICP, where the increasing pH (a result of calcite precipitation) further favors Sporosarcina. The key factor enabling this feedback appears to be the presence of a gas phase that includes oxygen. Our study provides the first direct evidence that gas-phase oxygen—not merely dissolved oxygen—is essential for enabling Sporosarcina dominance in soil microbial communities.

In summary, our study provides the first direct evidence that gas-phase oxygen—not merely dissolved oxygen—is essential for enabling Sporosarcina dominance in soil microbial communities. Even when oxygen is present in the atmosphere, the saturated soil conditions with fully water-filled pores prevent sufficient oxygen diffusion. The presence of a gas phase not only facilitates the oxygen transfer but also enables physicochemical dynamics (e.g., drying-rewetting, pH shifts) that reinforce Sporosarcina proliferation. This finding highlights a critical environmental requirement that has been previously underappreciated in the field of biostimulation-based MICP.

3.5. Integrating Present Results with Previous Knowledge and Applications

3.5.1. Reconciling with Previous Research on Sporosarcina Under Oxygen-Limited Conditions

Previous studies have described Sporosarcina’s ability to survive and function under oxygen-limited conditions. For instance, Kwon et al. (2007) demonstrated that Sporosarcina can perform anaerobic respiration using nitrate as an alternative electron acceptor, highlighting its metabolic flexibility [13]. Tobler et al. (2011) further confirmed that S. pasteurii can contribute to calcite precipitation in anoxic environments when bioaugmented into engineered systems [34]. At first glance, our findings—namely, the absence of Sporosarcina dominance in both aerobic and anaerobic saturated soils, as well as its failure to dominate in anaerobic unsaturated soil—may appear contradictory to these reports.

However, this apparent discrepancy offers a deeper insight into the environmental adaptation and ecological competitiveness of Sporosarcina. The distinction between mere survival and ecological dominance is critical. Although Sporosarcina can persist in oxygen-limited environments, as indicated by previous pure-culture studies and engineered applications [13,34], our data show that it does not achieve dominance in complex soil microbial communities under such conditions when relying on indigenous populations. This suggests that Sporosarcina’s effectiveness in MICP through biostimulation (i.e., enriching native populations) is highly dependent on oxygen availability. Moreover, while previous reports have shown the feasibility of MICP via bioaugmentation under controlled anaerobic conditions (e.g., Mortensen et al., 2011), these often involve high cell densities and nutrient delivery systems that do not reflect natural soil settings [11]. In contrast, our study focused on the behavior of indigenous Sporosarcina populations in soil reactors under varying oxygen and saturation conditions.

Although previous studies have explored Sporosarcina under defined oxygen-limited conditions using pure cultures or engineered systems, there remains a notable lack of in situ investigations that track its population dynamics across varying oxygen and saturation regimes in soil. To the best of our knowledge, this study represents the first attempt to fill that gap.

In summary, our findings do not contradict previous reports, but rather refine their applicability by demonstrating that while Sporosarcina can survive in oxygen-limited settings, it does not readily dominate under such conditions without external intervention. This highlights the importance of the environmental context—particularly the oxygen availability—when assessing the potential of Sporosarcina in MICP-based soil stabilization.

3.5.2. Implications for Biostimulation Strategies in Soil Stabilization

Our findings have practical implications for the application of MICP in real-world soil stabilization efforts, particularly in environments where oxygen availability is restricted. While bioaugmentation approaches have demonstrated that Sporosarcina can induce calcite precipitation even under anoxic conditions, our data suggest that such outcomes may not be readily replicated when relying on native microbial communities. The success of biostimulation-based strategies thus critically depends on the presence of an oxygen-containing gas phase within the soil matrix.

This is especially relevant for deep, compacted, or saturated soils, where oxygen diffusion is inherently limited. In such cases, the introduction of oxygen or the maintenance of air-filled pore spaces may be necessary to facilitate the Sporosarcina enrichment and functional activity. Our results from the saturated soil experiments further indicate that simply supplying oxygen in the headspace is insufficient; direct gas exchange within the soil environment appears to be a prerequisite for microbial dominance and effective MICP. Moreover, the presence of a gas phase not only improves the oxygen penetration but may also trigger physicochemical changes—such as drying-rewetting cycles—that favor calcite precipitation and microbial selection. A feedback loop between the calcite-induced alkalinity and Sporosarcina proliferation could further reinforce this effect, underscoring the importance of designing soil stabilization systems that support both geochemical and microbiological synergies. This nuanced understanding aligns with the observations reported by Gat et al. (2016) regarding the role of pH in regulating the microbial community dynamics in MICP [35], although our findings highlight the conditional nature of this feedback in promoting a dominance by Sporosarcina.

In light of these findings, future MICP implementations should carefully consider site-specific gas-phase conditions and soil structure. Tailoring biostimulation strategies to promote oxygen availability—either through engineered aeration, unsaturated flow regimes, or co-inoculation with oxygen-generating microbes—could enhance the reliability and efficiency of calcite-based soil reinforcement in challenging subsurface environments. Furthermore, the development of Sporosarcina strains with enhanced activity under oxygen-limited conditions—through non-GMO approaches such as laboratory evolution or environmental selection—may offer a practical path forward for expanding the MICP applicability without introducing regulatory complications. Recent advances in laboratory evolution platforms [36], including specialized devices designed for precise environmental control and long-term microbial cultivation, have opened up new possibilities for directed strain improvement in such contexts. These integrated approaches represent promising directions for future research and field deployment.

4. Conclusions

This study comprehensively examined the role of Sporosarcina in MICP under varying oxygen conditions, focusing on both saturated and unsaturated soils. Our findings indicate that Sporosarcina’s ability to dominate the microbial community and effectively contribute to the MICP is significantly affected by the presence of the oxygen. Although Sporosarcina can adapt to anaerobic conditions, its predominance and the efficacy of MICP are markedly enhanced in aerobic environments. This underscores the critical role of oxygen in facilitating the metabolic processes underlying the calcite precipitation mediated by Sporosarcina. Furthermore, our investigation of microbial dynamics in saturated soils identified several limitations to MICP under anaerobic conditions and highlighted the necessity of a gas phase for Sporosarcina to achieve dominance. Taken together, the results suggest that while Sporosarcina can survive without oxygen, the application of the MICP for soil stabilization in anaerobic environments requires a careful consideration of microbial community interactions and environmental conditions conducive to calcite precipitation.

In practical terms, these results highlight a key challenge in field-scale biostimulation: Sporosarcina dominance does not automatically follow urea injection in oxygen-limited or saturated conditions. Future work should explore the use of mixed microbial consortia or the enrichment of ureolytic strains with enhanced tolerance to low-oxygen conditions. One promising approach is the use of the laboratory-evolved strains developed through experimental evolution or directed selection. Such strains—unmodified at the genetic engineering level—may achieve better persistence and activity under oxygen-limited conditions, while remaining compatible with current regulatory frameworks. Investigating strategies such as controlled oxygen delivery, micro-aeration, or soil structuring to maintain partial gas phases may also improve the outcomes in subsurface applications.